| Type: | Undergraduate Subject |
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| Code: | DTSC13-301 |
| EFTSL: | 0.125 |
| Faculty: | Bond Business School |
| Semesters offered: |
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| Credit: | 10 |
| Study areas: |
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| Subject fees: |
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Description
Computer vision, natural language processing and personalised recommendations are just a few of the uses of artificial neural networks that are increasingly relevant to real-world problems that pose challenges for traditional data analysis techniques. This subject introduces students to the foundational ideas associated with the many variations of these models that have been developed for domains involving image data, temporal data, and natural language. This includes feed-forward, fully connected neural networks, convolutional neural networks, recurrent neural networks, and the transformer architecture. Class discussions will introduce the technical underpinnings of the models and applied sessions and assessments provide students the opportunity to experiment and apply them to a wide range of practical, real-world problems using Python.
Subject details
Learning outcomes
- Apply statistical techniques and mathematical reasoning to formulate machine learning tools for data analysis and appropriately explain key structural components and fitting algorithms.
- Design and train neural networks, including convolutional and recurrent structures and other modern extensions, and accurately identify key structural components and their significance.
- Apply neural networks to business data systems and other circumstances, such as image recognition and natural language processing.
- Articulate machine learning ideas, decisions, recommendations and other information in a clear, concise writing style tailored to a given audience.
- Demonstrate an appropriate awareness of global issues impacting decision-making paradigms and model-building exercises.
- Apply appropriate professional standards and best practices to make ethical, responsible decisions, decision-making paradigms and model-building exercises.
Enrolment requirements
| Requisites: |
Pre-requisites:Co-requisites:There are no co-requisites |
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| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge. Assumed Prior Learning (or equivalent):Possess demonstrable knowledge in elementary probability theory, statistics, elementary calculus and linear algebra to the level of a unit such as Quantitative Methods. |
| Restrictions: |
This subject is not available to
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Subject outlines
Subject dates
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May 2024
Standard Offering Enrolment opens: 17/03/2024 Semester start: 13/05/2024 Subject start: 13/05/2024 Cancellation 1: 27/05/2024 Cancellation 2: 03/06/2024 Last enrolment: 26/05/2024 Withdraw - Financial: 08/06/2024 Withdraw - Academic: 29/06/2024 Teaching census: 07/06/2024
| Standard Offering | |
|---|---|
| Enrolment opens: | 17/03/2024 |
| Semester start: | 13/05/2024 |
| Subject start: | 13/05/2024 |
| Cancellation 1: | 27/05/2024 |
| Cancellation 2: | 03/06/2024 |
| Last enrolment: | 26/05/2024 |
| Withdraw - Financial: | 08/06/2024 |
| Withdraw - Academic: | 29/06/2024 |
| Teaching census: | 07/06/2024 |